Economic analysis in retail science, and in similar endeavors such as wholesale science, can have many practical applications. For example, one area of study in retail science is the production of a forecast of sales units for merchandise to determine how many units of a particular piece of merchandise will sell in a particular time period.
The sales units of merchandise can be affected by many factors, such as seasonal factors. Seasonal factors can take into account things like temperature factors with respect to clothing sales, but also other scheduled events that trigger purchasing, such as the Christmas shopping season for items purchased as gifts or the start of classes at the end of summer for items purchased for school.
Other factors can include whether a discount has been applied to the merchandise during the time period and at what point in the life cycle of the merchandise the time period falls. These are not an exhaustive list of factors.
These and other factors can be combined together to create a model for demand. The model for demand can then be used to intelligently suggest or reasonably select from those factors that are within the control of the retailer (in the case of retail science) or the manufacturer/distributor (in the case of wholesale science).
The model for demand can include demand parameters which depend on demand variables. However, determining the relationship between demand parameters and demand variables may not be completely intuitive. In particular, while it may be valuable to pool many disparate units together to obtain a demand parameter based on the most possible data, such a pool may not be as precise as a pool constructed only of similar units.
Simply pooling similar units together can be imprecise and can lead to a lack of confidence with respect to whether the resulting demand parameter for a pool that does not have a sufficiently reliable demand parameter of its own has been reliably calculated. This approach may, therefore, be prone to error, and may require a relatively sophisticated user or a user with specific product intuition, thus limiting the user base of the software that calculates demand parameters.
Attempting to improve demand parameters conventionally requires very complex calculations, relying on simulation or search techniques. These techniques require large amounts of computational resources, making them impractical or at least very expensive for retail-science applications, since large retailers typically have a very large amount of historical data.